Spring Cloud DataFlow 2.4 is GA - With A Shiny New User Experience

February 11, 2020 Sabby Anandan

We're pleased to announce the general availability of Spring Cloud Data Flow 2.4.

Hot on the heels of  Spring Cloud Data Flow 2.3, this release delivers a fresh UI/UX, improved workflow, and a foundation for a fluid, intuitive developer experience now and for future releases.

In 2.4, the same powerful, core functionality of the Spring Cloud Data Flow dashboard is reassuringly familiar, but developers will also find the Dashboard more fluid, easier to use and quicker to deliver results.

The saying goes, “A picture is worth a thousand words” and user experience is a visual thing, so we’re going to show you what’s new in 2.4.

For the in-depth, detailed experience, watch the video: 

However, if you’re short on time, we grabbed a few teaser shots:

As you can see, version 2.4 features a richer look and feel with enhanced UI controls that serve as a foundation for further enhancements shipping in future releases.

Notable updates

In addition to UX enhancements, there’s more to like with the GA of 2.4:

Java DSL for tasks: Developers increasingly want to create data pipelines programmatically. This was already possible for streaming applications but not for batch. This release has closed this gap, with feature parity for both streaming and batch applications. This also improves support for a variety of use-cases, including CI/CD automation and streaming/batch data pipeline migration between sandbox, staging, and production environments.

Scheduling in shell: Scheduling is a superpower, and already accessible through APIs, Java DSL, and the interactive Dashboard. But what about shell commands? Thanks to another excellent contribution from Daniel Serleg you can now also schedule tasks directly from the SCDF’s shell application.

Manual / auto-scaling recipes: We presented our take on dynamic scaling of credit card fraud detection at SpringOne 2019. Building on that foundation, we have now published a step-by-step recipe for both manual and auto-scaling of streaming architectures. Take it for a spin and let us know what you think!

Shipping with confidence: We’re always looking for ways to invest in the quality of the Spring Cloud Data Flow platform, and 2.4 is the first release to benefit from the latest round of improvements. We’ve significantly increased test coverage and automation so our integration tests run faster and now target all of the supported platforms: local machine, Cloud Foundry, and Kubernetes.

Get Started with Spring Cloud Data Flow

Visit the Spring Cloud Data Flow microsite for installation on Kubernetes, Cloud Foundry or on your local machine.

Join our community!

We can’t wait for you to try out Spring Cloud Data Flow 2.4. If you want to ask questions or give us feedback, please reach out to us on Gitter, StackOverflow, or GitHub.

About the Author

Sabby Anandan

Sabby Anandan is a Product Manager on the Spring Team at VMware. He focuses on building products that address the challenges faced with iterative development and operationalization of data-intensive applications at scale. Before joining VMware, Sabby worked in engineering and management consulting positions. He holds a Bachelor’s degree in Electrical and Electronics from the University of Madras and a Master’s in Information Technology and Management from Carnegie Mellon University.

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